Note

Click here to download the full example code or to run this example in your browser via Binder

5.6 groupby#

This lesson shows the usage of groupby in pandas

Important

This lesson is still under development.

The material in this lesson is mostly inspired from pandas documentation for groupby and realpython .

import time

import numpy as np
import pandas as pd

print(time.asctime())
print(np.__version__)
print(pd.__version__)

Mon Nov 11 07:57:11 2024
1.26.4
1.5.3

url = "https://danepubliczne.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_hydrologiczne/dobowe/2020/codz_2020_01.zip"

df = pd.read_csv(
    url, compression='zip', encoding="ISO-8859-1",  engine='python',
    on_bad_lines="skip",
    names=['stn_id', 'year', 'day', 'water_level_cm', 'q_cms', 'temp_C', 'month'],
    usecols=[0, 3, 5, 6, 7, 8, 9],
    dtype={'stn_id': int, 'year': 'int', 'day': 'int', 'water_level_cm': np.float32, 'q_cms': np.float32, 'temp_C': np.float32, 'month': 'int'},
    parse_dates={'date': ['year', 'month', 'day']},
    index_col='date'
    )

df
stn_id water_level_cm q_cms temp_C
date
2020-11-01 149180020 82.0 12.90 99.900002
2020-11-02 149180020 80.0 12.50 99.900002
2020-11-03 149180020 79.0 12.10 99.900002
2020-11-04 149180020 81.0 12.60 99.900002
2020-11-05 149180020 84.0 13.40 99.900002
... ... ... ... ...
2020-11-26 149190250 142.0 0.44 99.900002
2020-11-27 149190250 142.0 0.44 99.900002
2020-11-28 149190250 142.0 0.44 99.900002
2020-11-29 149190250 142.0 0.44 99.900002
2020-11-30 149190250 142.0 0.44 99.900002

24913 rows × 4 columns



print(df.shape)

(24913, 4)

df['stn_id'].unique()

array([149180020, 149180300, 149180010, 150180060, 150180030, 150170240,
       150170290, 150170130, 150170090, 150170040, 151170030, 151160170,
       151160150, 151160130, 151160060, 151150150, 152150130, 152150050,
       152140130, 152140090, 152140050, 152140060, 152140020, 152140010,
       153140020, 153140030, 153140050, 153140190, 150170160, 150170170,
       149180130, 149180060, 149180070, 149180030, 149180050, 149180040,
       150180040, 150180090, 150180280, 150180130, 150180110, 150180140,
       150180120, 150180080, 150180250, 150180220, 150180180, 150180150,
       150180070, 150180170, 150180160, 150180010, 150170180, 150170110,
       150170080, 150170220, 150180190, 150180100, 150180050, 150180020,
       150180230, 150160190, 150160170, 150160180, 150160220, 150170060,
       150170100, 150170140, 150160210, 150160150, 150160230, 150160200,
       150160110, 150160080, 150160100, 150160270, 150170070, 150170050,
       150170120, 150170150, 150170200, 150170210, 150170010, 150170030,
       150160250, 150160280, 151160230, 150160060, 150160070, 150160120,
       150160160, 151160190, 150160140, 150160130, 150160290, 150160030,
       150160090, 151160180, 150160020, 151170090, 151170050, 151170010,
       151150170, 151160020, 151160050, 151160100, 151160090, 151160070,
       151160040, 151160080, 151150160, 151150180, 151170070, 151170040,
       151160140, 151170080, 151170060, 151160220, 151160200, 151160160,
       151160030, 151160260, 151150190, 152150170, 151160250, 151160010,
       152150060, 150150120, 150150130, 150160010, 150150100, 150150080,
       150150060, 151150140, 151150120, 151150080, 151150050, 151150040,
       152150020, 150150090, 150150110, 150150030, 150150050, 150150070,
       150150190, 150150200, 150150040, 151150130, 150150010, 151150110,
       151150060, 151150100, 151150090, 150150020, 151150070, 151150030,
       150140010, 150140020, 151140060, 151140040, 151140010, 150140100,
       150140030, 151150020, 151140050, 151150010, 151140030, 151140020,
       152140110, 152140080, 150190240, 150190200, 150190150, 150190220,
       151190060, 151180130, 151180100, 151180120, 151180080, 151180110,
       152180120, 152180050, 152170130, 152170080, 152170060, 152170010,
       152160140, 152160100, 152160050, 152150200, 152150110, 152150080,
       152150040, 152150010, 152140070, 150180210, 151190010, 151180090,
       151190020, 151180170, 151180140, 151190030, 151180180, 151180150,
       151190040, 151180160, 152180150, 152180140, 152180110, 152180160,
       152180060, 152170150, 152170140, 152170160, 152170190, 152170120,
       151180070, 151180040, 151180020, 151170110, 151180030, 151180060,
       151180010, 151180050, 152160090, 152160130, 152160060, 152169998,
       152160150, 152170020, 152170050, 152170030, 152170040, 152160110,
       152160120, 152160170, 152160080, 152160040, 152150270, 152150220,
       152150100, 152160030, 152150120, 152180100, 152180090, 152180170,
       152180080, 152180030, 153170100, 153170010, 153160170, 153160160,
       152160070, 152160010, 152150190, 152150140, 152150090, 152180010,
       152180020, 152170100, 152170090, 152170110, 153170040, 153160300,
       153160200, 153160210, 153160180, 153160150, 153160220, 153160260,
       153160250, 153160050, 153160290, 153160270, 153160070, 153160190,
       153160110, 153160060, 153170160, 153170020, 153170150, 153160280,
       153160030, 153160040, 153150120, 153150140, 153150100, 152150240,
       153160010, 153150180, 153150160, 153150150, 153150170, 152150150,
       152150180, 152150230, 152140120, 152140100, 152140030, 152140270,
       153140110, 153140200, 153140100, 153150010, 153140090, 153150060,
       153150020, 153140080, 153140060, 153150090, 153150080, 153150050,
       154150010, 153150030, 153150070, 154150020, 153160080, 153160020,
       154150050, 154150040, 154160080, 154160020, 153160240, 154160010,
       154160030, 154160040, 154160050, 154170040, 154160120, 154160070,
       154160130, 154160100, 154160090, 154160060, 154170120, 154170070,
       154170010, 154160140, 154170030, 154170020, 154170140, 154170230,
       154170150, 154170080, 154170060, 154170050, 154180020, 154170160,
       154170110, 154170090, 154170210, 154170130, 154180050, 154180030,
       154180080, 154180070, 150160040, 149180200, 149180180, 149180160,
       149180140, 149180110, 149180100, 149180080, 149180210, 149180240,
       149190060, 150190140, 150190170, 150190360, 150190260, 149190230,
       150190340, 150200060, 150200100, 150200130, 150200150, 150210020,
       150210150, 150210170, 150210190, 150210180, 151210190, 151210120,
       151210050, 152210040, 152210170, 152200110, 152200150, 152200030,
       152190120, 152190030, 153180090, 153180020, 153180080, 153180100,
       154180150, 154180220, 154180190, 154180200, 154180210, 154190040,
       154190020, 154180160, 154180170, 154180180, 149180120, 149180250,
       149180230, 149190030, 149190010, 149180090, 149180220, 150190060,
       150190050, 150190190, 150190120, 150190130, 150190080, 150190180,
       150190210, 150190010, 150180270, 150180260, 150190070, 150190270,
       150190250, 150190100, 149190050, 149190080, 149190100, 149190120,
       150190160, 149190040, 149190020, 149190090, 149190150, 149190070,
       149190140, 149190290, 149190260, 149190210, 149190180, 149190170,
       149190190, 149190220, 149190200, 149190160, 149190270, 150190310,
       150190330, 149190340, 149200060, 149200040, 149190310, 149200090,
       149200170, 149200080, 149190370, 149190350, 149200130, 150200070,
       150200140, 150200110, 149190280, 149200030, 149200050, 149200140,
       149200160, 149200190, 149200240, 149200230, 149200280, 150200170,
       149190300, 149190360, 149190390, 149190380, 149200020, 149190320,
       149200010, 149200100, 149200110, 149200070, 149200120, 149200180,
       149200150, 149200300, 149200290, 149200220, 149200270, 149200250,
       149200260, 149200200, 149200310, 149200330, 149200320, 150200010,
       150200030, 150200080, 150200020, 150200160, 150200120, 150200040,
       150200090, 150200050, 150210070, 150210010, 150210060, 150210100,
       150210030, 150210050, 149210090, 149210070, 149210050, 149210040,
       150210130, 150210120, 149210010, 149210030, 149210060, 149210020,
       149210100, 149210080, 150210110, 150210140, 150210160, 150210200,
       149220150, 149220130, 149220060, 149220030, 149220040, 149220190,
       150220100, 150220090, 150220070, 150220030, 150210210, 149220180,
       149220140, 149220080, 149220100, 149220110, 149220070, 149220020,
       149220050, 149220160, 149220200, 149220210, 150220140, 150220130,
       149210150, 149210160, 149210110, 149210130, 150220010, 150220080,
       149220010, 149210140, 149210120, 150220060, 150220040, 150220160,
       150220050, 150220110, 150220020, 151210130, 151200100, 151210010,
       151210020, 151210040, 150210090, 151210090, 150210040, 150210080,
       150210220, 151210080, 151220040, 150220170, 150230080, 150230010,
       150230020, 150230040, 151230010, 151220090, 151220010, 150220120,
       150230030, 150230050, 150230060, 151220130, 151220140, 151220070,
       151220100, 151220110, 151220080, 151220120, 151220060, 151220050,
       151200110, 151210060, 151210030, 151210110, 151210100, 151210070,
       150190280, 151190090, 151190100, 151200020, 151200090, 151200120,
       150190350, 151200060, 151190120, 151200070, 151190080, 151190110,
       151200040, 151200080, 152210070, 152210020, 152230120, 152230110,
       152230090, 152230040, 152220080, 153220270, 153220130, 153220070,
       153220010, 153210210, 153210090, 152210060, 152200130, 152230190,
       152230100, 152230060, 152230030, 153230160, 153230170, 153230110,
       153230010, 153230140, 153230080, 153230060, 153220230, 153220180,
       153230070, 153220260, 153220170, 153220100, 153230130, 154220070,
       153220240, 153220300, 153220280, 153220290, 153230050, 153230040,
       153230020, 154220060, 153220110, 153220120, 153220150, 153220190,
       153220320, 153220200, 153220220, 154220020, 154220030, 153220060,
       153220050, 153220080, 153220140, 153220160, 153220310, 153220090,
       154210090, 153210110, 153210130, 153210200, 153210190, 153210170,
       153210220, 154210040, 153210100, 153210050, 153210080, 153210240,
       153210230, 153210180, 153210140, 153210040, 153210120, 153210010,
       153210070, 153200160, 152210100, 153210020, 152210030, 150240020,
       150240010, 151230060, 151230040, 152230080, 152230200, 152220050,
       152229999, 152210090, 152210150, 150230070, 151230050, 151230030,
       151230080, 152230020, 152230070, 152230050, 151230020, 152220060,
       152230010, 152220070, 152220030, 152220090, 152210130, 152220010,
       152210120, 152210140, 152210160, 152210050, 153190160, 152200020,
       152200120, 153200050, 153200020, 152200100, 152200060, 152200080,
       152190050, 152199992, 152200050, 151190070, 152190100, 152200010,
       152200090, 152200070, 152190060, 152190080, 152190110, 152190020,
       152190040, 152180130, 153200010, 153190170, 153190140, 153190120,
       153190090, 153190050, 153180140, 153190080, 153190100, 153190150,
       153190130, 153190180, 153190070, 153190190, 153190060, 153170050,
       153170080, 153170060, 153170120, 153170140, 153179996, 153170070,
       153170110, 153170090, 154170180, 153170130, 153180010, 153180050,
       153180060, 153180040, 153190020, 153180150, 153190010, 154180010,
       153180030, 154180110, 153180110, 154179991, 154170190, 154180230,
       154170240, 154180040, 154180060, 154180270, 154180260, 154190010,
       153190030, 153180130, 153190040, 154190080, 154190060, 154190100,
       154190110, 153200040, 153200030, 154190170, 154190150, 154190160,
       154190140, 154200020, 154200010, 153200070, 154200030, 154210010,
       153200140, 153200090, 154200040, 154210020, 154210030, 154210060,
       154210070, 154210080, 154210100, 154220050, 154220110, 154220010,
       154220100, 154230010, 154220080, 154220090, 154230020, 154230030,
       153230120, 153230090, 154230040, 149220170, 149190240, 149190250])

df['stn_id'].nunique()

840

grouper = df.groupby('stn_id')

grouper

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f0c7f2ef7f0>

type(grouper)

grouper.ngroups

840

grouper.groups

{149180010: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149180020: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149180030: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149180040: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149180050: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149180060: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149180070: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 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2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149200320: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149200330: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149210010: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149210020: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149210030: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149210040: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149210050: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149210060: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149210070: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149210080: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149210090: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], ...}

grouper.get_group(149180020)
stn_id water_level_cm q_cms temp_C
date
2020-11-01 149180020 82.0 12.9 99.900002
2020-11-02 149180020 80.0 12.5 99.900002
2020-11-03 149180020 79.0 12.1 99.900002
2020-11-04 149180020 81.0 12.6 99.900002
2020-11-05 149180020 84.0 13.4 99.900002
... ... ... ... ...
2020-11-26 149180020 90.0 15.3 99.900002
2020-11-27 149180020 91.0 15.8 99.900002
2020-11-28 149180020 90.0 15.3 99.900002
2020-11-29 149180020 87.0 14.4 99.900002
2020-11-30 149180020 86.0 14.0 99.900002

30 rows × 4 columns



type(grouper.get_group(149180020))

grouper.get_group(149180020).shape

(30, 4)

grouper.get_group(149180020).head()
stn_id water_level_cm q_cms temp_C
date
2020-11-01 149180020 82.0 12.9 99.900002
2020-11-02 149180020 80.0 12.5 99.900002
2020-11-03 149180020 79.0 12.1 99.900002
2020-11-04 149180020 81.0 12.6 99.900002
2020-11-05 149180020 84.0 13.4 99.900002 


grouper.get_group(149180020).tail()
stn_id water_level_cm q_cms temp_C
date
2020-11-26 149180020 90.0 15.3 99.900002
2020-11-27 149180020 91.0 15.8 99.900002
2020-11-28 149180020 90.0 15.3 99.900002
2020-11-29 149180020 87.0 14.4 99.900002
2020-11-30 149180020 86.0 14.0 99.900002 


for name, group in grouper:
    print(name, group.shape)
    break

149180010 (30, 4)

for idx, (name, group) in enumerate(grouper):
    print(name, group.shape)
    if idx > 5:
        break

149180010 (30, 4)
149180020 (30, 4)
149180030 (30, 4)
149180040 (30, 4)
149180050 (30, 4)
149180060 (30, 4)
149180070 (30, 4)

for idx, (name, group) in enumerate(grouper):
    pass

print(idx)

839

What is the mean discharge for each station?

for idx, (name, group) in enumerate(grouper):
    print(name, group['q_cms'].mean())
    if idx > 5:
        break

149180010 25.900002
149180020 17.079998
149180030 7.032334
149180040 0.58933336
149180050 0.6753333
149180060 3.5083337
149180070 0.15820001

What is the mean, min and max temperature for each station?

for idx, (name, group) in enumerate(grouper):
    print(name, group['temp_C'].mean(), group['temp_C'].min(), group['temp_C'].max())
    if idx > 5:
        break

149180010 99.89999 99.9 99.9
149180020 99.89999 99.9 99.9
149180030 99.89999 99.9 99.9
149180040 99.89999 99.9 99.9
149180050 99.89999 99.9 99.9
149180060 99.89999 99.9 99.9
149180070 99.89999 99.9 99.9

replace values > 99.8 and less than 100 with np.nan

df.loc[(df['temp_C']>99.8) & (df['temp_C']<100.0), 'temp_C'] = np.nan

for idx, (name, group) in enumerate(grouper):
    if not np.isnan(group['temp_C'].mean()):
      print(name, group['temp_C'].mean(), group['temp_C'].min(), group['temp_C'].max())
    if idx > 50:
        break

149180100 7.933334 5.6 10.9
149180240 5.520001 5.0 6.3
149190240 4.55 2.0 7.5
149190310 9.059999 8.2 10.6

Total running time of the script: ( 0 minutes 1.289 seconds)

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